The value of bioelectrical impedance analysis vs. condition indices in predicting body fat stores in North American porcupines (Erethizon dorsatum)

Abstract

We developed a predictive model to estimate body fat stores in a population of North American porcupines, Erethizon dorsatum (L., 1758). We trapped porcupines in the autumn of 2004 and spring of 2005. After collecting morphometric measurements on each animal, we used a plethysmograph to perform bioelectrical impedance analysis (BIA). We euthanized the subjects, measured two components of body composition (body fat, body water) via direct chemical analysis, and calculated lean dry mass to compare with BIA data. With regression we found the best predictive models for total body water, total body fat, percent body fat, and lean dry mass. We also estimated body condition for each animal using six different condition indices and compared the ability of the condition indices and our regression model to predict total body fat. Our model for total body fat accounted for 84% of the variation in fat measured by direct chemical analysis, and our model for percent body fat accounted for 78% of the variation. Two condition indices were significantly related to total body fat in porcupines and explained 45%-49% of the variation in observed body fat. We recommend BIA as a useful technique for estimating body fat stores in field studies of free-ranging porcupines and suggest abandonment of the use of condition indices as analogues of body fat stores in animal studies unless the indices can first be validated.Résumé : Nous avons mis au point un modèle prédictif pour estimer les réserves de graisses corporelles chez une population de porcs-épics nord-américains, Erethizon dorsatum (L., 1758). Nous avons attrapé les porcs-épics au piège à l'automne 2004 et au printemps 2005. Après avoir pris les mesures morphométriques de chaque animal, nous avons mesuré l'impédance bioélectrique à l'aide d'un pléthysmographe. Nous avons ensuite euthanasié les animaux, mesuré deux composantes de leur composition corporelle (graisses corporelles et contenu hydrique) par analyse chimique directe, calculé la masse sèche sans les graisses et comparé ces données à celles de la méthode de l'impédance bioélectrique (BIA). À l'aide de régressions, nous avons trouvé les meilleurs modèles prédictifs du contenu hydrique corporel total, des graisses corporelles totales, du pourcentage de graisses corporelles et de la masse sèche sans les graisses. Nous avons aussi estimé la condition corporelle de chaque animal à l'aide de six coefficients différents de condition et comparé la capacité de prédic-tion du contenu corporel total de graisses de ces coefficients de condition et celle de notre modèle de régression. Notre modèle du contenu corporel total de graisses explique 84 % de la variation des graisses mesurées par analyse chimique directe et notre modèle du pourcentage corporel des graisses explique 78 % de la variation. Deux des coefficients de condition sont en corrélation directe avec les graisses corporelles totales chez les porcs-épics et ils expliquent 45 % -49 % de la variation dans les graisses corporelles observées. Nous recommandons la méthode BIA co...

Morphometric indices of body condition in birds: a review

“…Such studies are scarce, and it remains unclear whether ratios are better indicators of fat mass, residuals are better indicators of fat mass, or they are similar in their ability to predict fat mass (Conway et al 1994;Schamber et al 2009). This holds true not only for birds but for mammals as well (Schulte-Hostedde et al 2005;Barthelmess et al 2006;Pitt et al 2006). Another way to assess how well a given index performs is to use a cross-validation approach.…”

Immediate effects of capture on nest visits of breeding blue tits, Cyanistes caeruleus, are substantial

“…Condition was calculated as the ratio of weight over tarsus (Barthelmess, 2006;Labocha, Schutz, & Hayes, 2014;Pitt, Larivi ere, & Messier, 2006). There was no evidence that weightetarsus allometry influenced condition estimates: fitting a curve to the weight over tarsus via a cubic smoothing spline (R-package 'fields' ;Furrer, Nychka, & Sain, 2013) resulted in a straight line; no transformation of variables was necessary to meet assumptions of homogeneity of variance and normality in the Model 1 (ordinary least squares) regression of weight over tarsus (Jakob, Marshall, & Uetz, 1996).…”

Home Range, Body Condition, and Survival of Rehabilitated Raccoons (Procyon lotor) During Their First Winter

“…Indirect methods use standard morphological measurements to calculate a condition index (Barthelmess, Phillips, & Schuckers, 2006;Pitt et al, 2006a;Stringer, Stoskopf, Simons, O'Connell, & Waldstein, 2010;Woolnough, Foley, Johnson, & Evans, 1997). Although condition indexes do not measure body composition directly, they do allow for comparison between individuals in a population and across time (Barthelmess et al, 2006). Residual indexes were used to measure body condition (Barthelmess et al, 2006;Pitt et al, 2006a).…”

“…Although condition indexes do not measure body composition directly, they do allow for comparison between individuals in a population and across time (Barthelmess et al, 2006). Residual indexes were used to measure body condition (Barthelmess et al, 2006;Pitt et al, 2006a). A residual index is calculated by finding the regression residuals, the distance from the regression line to individual data points of a linear regression, of body mass versus TBL.…”

“…A residual index is calculated by finding the regression residuals, the distance from the regression line to individual data points of a linear regression, of body mass versus TBL. In this method, it is assumed that animals with better body conditions (i.e., more fat reserves) will have larger, positive residuals compared with animals with smaller fat reserves (Barthelmess et al, 2006).…”